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Selected problems in a vehicle-to-truck collision modeling

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A selected case of a frontal, eccentric and oblique road vehicle-to-truck collision has been analyzed here as an example of the potential resultant motion by the motor vehicle with the truck performing a planar motion. This case is a pure problem of collision mechanics, so the main aim of the case presented here was to identify the selected parameters of such a collision with the three components of the collision impulse of only one vehicle taking part in it. The problem was analyzed with the use of a computer simulation performed in a software called V-SIM which is quite popular as a tool for accident reconstruction by e.g., forensic experts and others associated with road safety, especially in Poland. The results of all simulations were important in providing the basics of mathematical modeling regarding collision mechanics. The obtained observations may further be used for a dedicated software, should such be created. Not only a normal and a tangential but also a bi-normal direction versus the adopted plane of collision was considered.
Czasopismo
Rocznik
Strony
199--208
Opis fizyczny
Bibliogr. 12 poz.
Twórcy
  • Warsaw University of Technology, Faculty of Administration and Social Sciences, Plac Politechniki 1, Warsaw, 00-661, Poland
  • Almot-Ekspert, Technical Department, Janusza Kusocińskiego 20, Niemcz, 86-032, Poland
  • Bydgoszcz University of Science and Technology, Faculty of Mechanical Engineering, al. prof. S. Kaliskiego 7, Bydgoszcz, 85-796, Poland
Bibliografia
  • 1. Aleksandrowicz, I. & Zalewski, J. & Aleksandrowicz, P. Selected problems in a two-vehicle impact collision modeling. 2022. Applied Science. Vol. 12(19). No. 9921. DOI: 10.3390/app12199921.
  • 2. Noorsumar, G. & Rogovchenko, S. & Robbersmyr, K. & Vysochinskiy, D. Mathematical models for assessment of vehicle crashworthiness: a review. Journal of Crashworthiness. 2022. Vol. 27. No. 5. P. 1545-1559. DOI: 10.1080/13588265.2021.1929760.
  • 3. Aleksandrowicz, P. Modeling head-on collisions: The problem of identifying collision parameters. 2020. Applied Science. Vol. 10(18). No. 6212. DOI: doi.org/10.3390/app10186212.
  • 4. Choi, J. & Kong, C. & Kim, G. & Lim, A. Car crash detection using ensemble deep learning and multimodal data from dashboard cameras. Expert Systems with Applications. 2021. Vol. 183. No. 115400. DOI: 10.1016/j.eswa.2021.115400.
  • 5. Fisa, R. & Musukuma, M. & Sampa, M. Effects of interventions for preventing road traffic crashes: an overview of systematic reviews. BMC Public Health. 2022. Vol. 22. No. 513. DOI: 10.1186/s12889-021-12253-y.
  • 6. Kamaluddin, N. & Andersen, C. & Larsen, M. Self-reporting traffic crashes - a systematic literature review. European Transport Research Review. 2018. Vol. 26. No. 10. DOI: 10.1186/s12544-018-0301-0.
  • 7. Hu, L. & Bao, X. & Wu, H. & Wu, W. A study on correlation of traffic accident tendency with driver characters using in-depth traffic accident data. Journal of Advanced Transportation. 2020. Vol. 2020. No. 9084245. DOI: 10.1155/2020/9084245.
  • 8. Ren, R. & Li, H. & Han, T. & Zhang, C. & Zhang, J. et al. Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways. Accident Analysis & Prevention. 2023. Vol. 186. No. 107021. DOI: 10.1016/j.aap.2023.107021.
  • 9. Radu, A. & Toganel, G. & Trusca, D. Mathematical model validated by a crash test to be used as kinematic and dynamic study for side impacts. International Journal of Automotive Technology. 2021. Vol. 22. P. 1267-1277. DOI: 10.1007/s12239-021-0111-6.
  • 10. Orilonise, A. & Muhammed, K. & Woli, T. & Ibrahim, L. Frontal car crash analysis using finite element modelling: a case study of Toyota Corolla '05 model. International Academic Journal of Information, Communication, Technology & Engineering. 2022. Vol. 8. No. 1. P. 104-114. Available at: https://www.accexgate.com.
  • 11. Wang, X. & Li, S. & Li, X. & Wang, Y. & Zeng, Q. et al. Effects of geometric attributes of horizontal and sag vertical curve combinations on freeway crash frequency. Accident Analysis & Prevention. 2023. Vol. 186. No. 107056. DOI: 10.1016/j.aap.2023.107056.12.
  • 12. Qijun, C. & Yuxi, X. & Yu, A. & Tiange, L. & Guorong, C. & Shaofei, R. & Chao, W. & Shaofan, L. A deep neural network inverse solution to recover pre-crash impact data of car collisions. Transportation Research Part C: Emerging Technologies. 2021. Vol. 126. No. 103009. DOI: 10.1016/j.trc.2021.103009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c6993d7d-e1cb-4605-82c8-23b0fbacfd0f
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